Digital trail making test-black and white: Normal vs MCI

Trail making test - black & white (TMT B&W): Normative study for the Chilean population

The trailmaking test (TMT) has an educational bias that makes it inapplicable to people with low levels of education due to its dependence on the alphabet. The TMT standardization is the only one available in Chile, and there is a need for alternative ways of using the TMT that do not depend on the level of education for its applicability. To determine the normative scores of the TMT - black & white (TMT B&W), considering sociodemographic factors in adult and elderly Chilean population. A total sample of 227 participants (133 healthy, 94 cognitively impaired) from the Ageing Mets cohort were recruited from three areas in Chile (Antofagasta, Santiago, and Puerto Montt). The TMT B&W was administered to all participants. A multiple regression model was used to generate normative data only in the cognitively healthy group, considering the effect of age, education and sex. A significant effect of age was found in the score of the TMT B&W Parts A and B. The level of education influenced the part B of the test; however, the completion rate of the TMT B&W parts A and B was over 90% in cognitively healthy people. Norms for the number of errors were obtained, and differences between groups were found after controlling for the effect of age and education. This study is the first to provide normative data for the Chilean version of the TMT B&W and will benefit clinical neuropsychologists by improving the procedures for more accurately assessing executive functions and its impairments.

Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

BACKGROUND

Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method.

OBJECTIVE

This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices.

METHODS

This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22).

RESULTS

We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data.

CONCLUSIONS

The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' cognitive impairment compared to conventional paper-based feature assessment.

The Trail Making Test (part B) is associated with working memory: A concurrent validity study

The Test Making Test (TMT) was originally created as a distributed attention test. Part B (TMT-B) has been proposed as representative of executive functions as effective problem solving and working memory. This study aimed to explore the validity of the TMT-B as an indicator of working memory in adults. A cross-sectional study was conducted by using linear correlation coefficients between the TMT-B and neuropsychological and electrophysiological tests of working memory. Fifty-six individuals participated, all of which had normal cognitive functioning and were aged between 19 and 55 years old. Results show a significant correlation among the TMT-B scores with all subtests, the overall score of the Corsi Block-Tapping Test, the Working Memory Index of the WAIS-IV (Wechsler Adult Intelligence Scale) (p ≤ .05) and the auditory Event Related Potentials (p < .01) with the N200 and P300 latencies and amplitudes. These findings are preliminary evidence of the validity of the TMT-B for the evaluation of working memory in adults. Additional studies are required to assess the differential validity of the TMT-B in the evaluation of working memory, through comparative correlational analyzes with the results of various neuropsychological tests that assess other cognitive functions.

Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review

The dementia population is increasing as the world's population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle-Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.